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Operationalizing includes the use of existing data to identify business opportunities, creating meaningful reports, data visualization, and develop AI/ML models that support the team’s AI pipeline.\nThe data analyst would also be collaborating with the automation developers, business analysts, and solutions\narchitects to build relevant data output.\nResponsibilities:\nDesigning data models - Create efficient and scalable data models to support analytical and operational needs.\nData Analysis - Interpret raw or staged data into business-ready data\nData Visualization - Create dashboards that effectively translates business data to insights\nData quality - Implement data validation and quality checks across the data management pipeline\nCollaboration - Collaborate with business analysts, developers, and business to understand data requirements and provide them with the necessary data\nData Quality - Produce and query data with high accuracy, completeness, consistency, and validity for usability in analysis and key decision-making.\nTime-to-Insight - Data for business insights must be delivered within a workable timeline to ensure relevance and value appreciation.\nQualifications:\n3+ years of experience as a data engineer, data analyst, or SQL developer\nAt least 1 year of experience with using Data Visualizations tools such as Tableau, PowerBI\nWorking knowledge of data warehousing concepts and technologies (e.g., Snowflake, Redshift, BigQuery); 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